code: def _create_examples(self, qa_file_path, set_type):
      wiki_df = pd.read_csv(
          self.path_dict["wiki_dict"],
          sep="\t",
          names=["sent_id", "text"],
      )
      wiki_dict = {
          row.sent_id: row.text
          for row in wiki_df.itertuples(index=False)
      }
  
      data_df = pd.read_csv(
          qa_file_path,
          sep="\t",
          header=None,
          names=[
              "sent_id",
              "target_ids",
              "worker_id",
              "qa_index",
              "qa_word",
              "question",
              "answer",
              "response1",
              "response2",
          ],
      )
      data_df["sent"] = data_df["sent_id"].apply(wiki_dict.get)
  
      examples = []
      ptb_detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()
      for i, row in enumerate(data_df.itertuples(index=False)):
          # Answer indices are a space-limited list of numbers.
          # We simply take the min/max of the indices
          answer_idxs = list(map(int, row.answer.split()))
          answer_token_start, answer_token_end = min(answer_idxs), max(
              answer_idxs
          )
          passage_ptb_tokens = row.sent.split()
          passage_space_tokens = ptb_detokenizer.detokenize(
              passage_ptb_tokens, convert_parentheses=True
          ).split()
          passage_space_str = " ".join(passage_space_tokens)
  
          token_aligner = TokenAligner(
              source=passage_ptb_tokens, target=passage_space_tokens
          )
          answer_char_span = token_aligner.project_token_to_char_span(
              answer_token_start, answer_token_end, inclusive=True
          )
          answer_str = passage_space_str[
              answer_char_span[0] : answer_char_span[1] + 1
          ]
  
          examples.append(
              span_pred_template.Example(
                  guid="%s-%s" % (set_type, i),
                  passage=passage_space_str,
                  question=row.question,
                  answer=answer_str,
                  answer_char_span=answer_char_span,
              )
          )
  
      return examples
  
file_path: jiant/jiant/tasks/lib/qamr.py
imports: import pandas
  import nltk
  from jiant.tasks.lib.templates import span_prediction
  from jiant.utils.retokenize import TokenAligner
label_desc: Use image node_id 4 for calling the QAMRTask obj's underlying member method code with example usage: obj._create_examples(qa_file_path, set_type) and returns: examples